Juniper Networks, Inc.

Mithun Chakaravarrti Dharmaraj

Harshit Naresh Chitalia

Tarun Banka

Yixiao Wei

Gauresh Dilip Vanjare

Rajenkumar Patel

Shruti Jadon

Thayumanavan Sridhar

Raj Yavatkar

Anita Kar

Rahul Gupta

Aditi Ghotikar

Venny Kranthi Teja Kommarthi

Keval Nimeshkumar Shah

A computing system comprising a memory and processing circuitry may perform the techniques. The memory may store time series data comprising measurements of one or more performance indicators. The processing circuitry may determine, based on the time series data, an anomaly in the performance of the network system, and create, based on the time series data, a knowledge graph. The processing circuitry may determine, in response to detecting the anomaly, and based on the knowledge graph and a machine learning (ML) model trained with previous time series data, a causality graph. The processing circuitry may determine a weighting for each edge in the causality graph, determine, based on the edges in the causality graph, a candidate root cause associated with the anomalies, and determine a ranking of the candidate root cause based on the weighting. The analysis framework system may output at least a portion of the ranking.

Framework for automated application-to-network root cause analysis

A telemetry service can receive telemetry collection requirements that are expressed as an 'intent' that defines how telemetry is to be collected. A telemetry intent compiler can receive the telemetry intent and translate the high level intent into abstract telemetry configuration parameters that provide a generic description of desired telemetry data. The telemetry service can determine, from the telemetry intent, a set of devices from which to collect telemetry data. For each device, the telemetry service can determine capabilities of the device with respect to telemetry data collection. The capabilities may include a telemetry protocol supported by the device. The telemetry service can create a protocol specific device configuration based on the abstract telemetry configuration parameters and the telemetry protocol supported by the device. Devices in a network system that support a particular telemetry protocol can be allocated to instances of a telemetry collector that supports the telemetry protocol.

Intent-based telemetry collection service with supported telemetry protocol in a virtualized computing infrastructure

Techniques for resource monitoring and managed message reordering in a data center are described. In one example, a computing system comprises an ingress engine to receive a message from a network device in a data center comprising a plurality of network devices and the computing system; and in response to receiving the message from a network device in the data center, communicate the message to an appropriate collector application corresponding to the message's protocol type in compliance with at least one requirement for data stored in a message flow communicated from one or more network devices to the computing system.

Data center resource monitoring with managed message load balancing with reordering consideration

This disclosure describes techniques that include using an automatically trained machine learning system to generate a prediction. In one example, this disclosure describes a method comprising: based on a request for the prediction: training each respective machine learning (ML) model in a plurality of ML models to generate a respective training-phase prediction in a plurality of training-phase predictions; automatically determining a selected ML model in the plurality of ML models based on evaluation metrics for the plurality of ML; and applying the selected ML model to generate the prediction based on data collected from a network that includes a plurality of network devices.

Machine learning pipeline for predictions regarding a network

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Mountain View, CA, United States of America

Mithun Chakaravarrti Dharmaraj